Adults with autism face many difficulties when finding employment, such as struggling with interviews and needing accommodating environments for sensory issues. However, autistic adults also have unique skills to contribute to the workplace that companies have recently started to seek after, such as close attention to detail and trustworthiness. To work around these difficulties and help companies find the talent they are looking for we have developed a job-matching system. Our system is based around the stable matching of the Gale-Shapley algorithm to match autistic adults with employers after estimating how both adults with autism and employers would rank the other group. The system also uses filtering to approximate a stable matching even with a changing pool of users and employers, meaning the results are resistant to change as the result of competition. Such a system would be of benefit to both autistic adults and employers and would advance knowledge in recommendation systems that match two parties.

In recent years, Recommendation Systems have revolutionized how social media and ecommerce are used. Fashion Recommendation Systems have made it easier for customers to do shopping, by recommending items to them based on various factors, such as their previous orders, and their similarities to other users. To apply a Fashion Recommendation System, there are four main approaches: Content-based filtering, where the system recommends similar items to the user. Collaborative filtering, in which the system recommends items from similar users. Hybrid filtering, which merges the features of the previous techniques, and Hyper-personalized filtering, which uses the profiling of customers to draw certain assumptions about users. The problem this research addresses is the lack of involving the intent of users when designing and applying a fashion recommendation system, as well as the cold start problem. The Research Questions are: 1. How to develop and implement a Fashion Recommendation System as an artifact that provides recommendations to customers, 2. How to implement intent as context in such Recommendation Systems to provide improved recommendations to the fashion customers, 3. How the inclusion of intent as context in a Fashion Recommendation System impacts customer satisfaction. The Research Methodology used in this study is design science research, with various research strategies and data collection methods used throughout, such as crowdsourcing, document analysis, testing, qualitative questionnaires, and thematic analysis. The Results of the study indicate the involvement of the intent results in better recommendations, a smoother and more accurate shopping experience, and an overall higher customer satisfaction.

Developers are often tasked with maintaining complex systems. Regardless of prior experience, there will inevitably be times in which they must interact with parts of the system with which they are unfamiliar. In such cases, recommendation systems may serve as a valuable tool to assist the developer in implementing a solution. Many recommendation systems in software engineering utilize the Stack Overflow knowledge-base as the basis of forming their recommendations. Traditionally, these systems have relied on the developer to explicitly invoke them, typically in the form of specifying a query. However, there may be cases in which the developer is in need of a recommendation but unaware that their need exists. A new class of recommendation systems deemed Behavior-Driven Recommendation Systems for Software Engineering seeks to address this issue by relying on developer behavior to determine when a recommendation is needed, and once such a determination is made, formulate a search query based on the software engineering task context. This thesis presents one such system, StackInTheFlow, a plug-in integrating into the IntelliJ family of Java IDEs. StackInTheFlow allows the user to intervi act with it as a traditional recommendation system, manually specifying queries and browsing returned Stack Overflow posts. However, it also provides facilities for detecting when the developer is in need of a recommendation, defined when the developer has encountered an error messages or a difficulty detection model based on indicators of developer progress is fired. Once such a determination has been made, a query formulation model constructed based on a periodic data dump of Stack Overflow posts will automatically form a query from the software engineering task context extracted from source code currently open within the IDE. StackInTheFlow also provides mechanisms to personalize, over time, the results displayed to a specific set of Stack Overflow tags based on the results previously selected by the user. The effectiveness of these mechanisms are examined and results based the collection of anonymous user logs and a small scale study are presented. Based on the results of these evaluations, it was found that some of the queries issued by the tool are effective, however there are limitations regarding the extraction of the appropriate context of the software engineering task yet to overcome.

La facilitation est un élément central dans une prise de décision de groupe surtout en faisant l'usage des outils de nouvelle technologie. Le facilitateur, pour rendre sa tâche facile, a besoin des solutions de vote pour départager les décideurs afin d'arriver à des conclusions dans une prise de décision. Une procédure de vote consiste à déterminer à partir d’une méthode le vainqueur ou le gagnant d’un vote. Il y a plusieurs procédures de vote dont certaines sont difficiles à expliquer et qui peuvent élire différents candidats/options/alternatives proposées. Le meilleur choix est celui dont son élection est acceptée facilement par le groupe. Le vote dans la théorie du choix social est une discipline largement étudiée dont les principes sont souvent complexes et difficiles à expliquer lors d’une réunion de prise de décision. Les systèmes de recommandation sont de plus en plus populaires dans tous les domaines de science. Ils peuvent aider les utilisateurs qui n’ont pas suffisamment d’expérience ou de compétence nécessaires pour évaluer un nombre élevé de procédures de vote existantes. Un système de recommandation peut alléger le travail du facilitateur dans la recherche d’une procédure vote adéquate en fonction du contexte de prise de décisions. Le sujet de ce travail de recherche s’inscrit dans le champ de l’aide à la décision de groupe. La problématique consiste à contribuer au développement d’un système d’aide à la décision de groupe (Group Decision Support System : GDSS). La solution devra s’intégrer dans la plateforme logicielle actuellement développée à l’IRIT GRUS : GRoUp Support. / Facilitation is a central element in decision-making, especially when using new technology tools. The facilitator, to make his task easy, needs voting solutions to decide between decision-makers in order to reach conclusions in a decision-making process. A voting procedure consists of determining from a method the winner of a vote. There are several voting procedures, some of which are difficult to explain and which may elect different candidate/options/alternatives proposed. The best choice is the one whose election is easily accepted by the group. Voting in social choice theory is a widely studied discipline whose principles are often complex and difficult to explain at a decision-making meeting. Recommendation systems are becoming more and more popular in all fields of science. They can help users who do not have sufficient experience or competence to evaluate large numbers of existing voting procedures. A recommendation system can lighten the facilitator's workload in finding an appropriate voting procedure based on the decision-making context. The objective of this research work is to design such recommendation system. This work is in the field of group decision support. The issue is to contribute to the development of a Group Decision Support System (GDSS). The solution will have to be integrated into the software platform currently being developed at IRITGRUS: GRoUp Support.

This report presents a survey of the state-of-the-art methods for building recommendation systems. The report mainly concentrates on systems that use the available side information in addition to a fraction of known affinity values such as ratings. Such data is referred to as Dyadic Data with Covariates (DyadC). The sources of side information being considered includes user/item entity attributes, temporal information and social network attributes. Further, two new models for recommendation systems that make use of the available side information within the collaborative filtering (CF) framework, are proposed. Review Quality Aware Collaborative Filtering, uses external side information, especially review text to evaluate the quality of available ratings. These quality scores are then incorporated into probabilistic matrix factorization (PMF) to develop a weighted PMF model for recommendation. The second model, Mixed Membership Bayesian Affinity Estimation (MMBAE), is based on the paradigm of Simultaneous Decomposition and Prediction (SDaP). This model simultaneously learns mixed membership cluster assignments for users and items along with a predictive model for rating prediction within each co-cluster. Experimental evaluation on benchmark datasets are provided for these two models. / text

Made available in DSpace on 2014-06-12T15:52:44Z (GMT). No. of bitstreams: 2 arquivo1844_1.pdf: 1599198 bytes, checksum: 84a19c5d7769a76fba813a0cac740509 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2009 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / As organizações, com o intuito de aumentarem o seu grau de competitividade no mercado, vêm a cada instante buscando novas formas de evoluir a produtividade e a qualidade dos produtos desenvolvidos, além da diminuição de custos que está diretamente relacionada ao aumento do faturamento líquido. Para que tais objetivos possam ser alcançados é primordial explorar ao máximo o potencial de seus colaboradores e os possíveis relacionamentos que esses colaboradores têm uns com os outros, ou seja, encontrar e partilhar conhecimento tácito. Como o conhecimento tático está na mente das pessoas, é difícil de ser formalizado e documentado, por isso, o ideal seria identificar e recomendar a pessoa que detém o conhecimento. Diante disso, a presente dissertação apresenta o Sistema de Recomendação de Especialistas SWEETS e a sua implantação no ambiente a.m.i.g.o.s., uma plataforma de gestão de conhecimento baseada em conceitos voltados às redes sociais. O SWEETS foi desenvolvido em duas versões, 1.0 e 2.0. A versão 1.0, de forma pró-ativa, aproxima pessoas com especialidades em comum, ora pelos seus conhecimentos (perfil de escrita), ora pelos seus interesses (perfil de leitura). Já a versão 2.0 do SWEETS não atua de forma pró-ativa, ou seja, é necessário que haja a requisição de um usuário especialista em determinada área, e é baseada em folksonomia para extração de uma ontologia, fundamental para identificar as especialidades das pessoas de forma mais eficaz. Esta ontologia é refletida pela co-ocorrência das tags (conceitos) em relação aos itens (instâncias) e é independente de domínio principal contribuição dessa dissertação. A implantação do SWEETS no a.m.i.g.o.s. visa trazer benefícios como: minimizar o problema de comunicação na corporação, prover um incentivo ao conhecimento social e partilhar conhecimento; proporcionando, assim, à empresa, a utilização mais eficaz dos conhecimentos de seus colaboradores

Today, commercial industry spends huge amount of resources in advertisement campaigns, new marketing strategies, and promotional deals to introduce their product to public and attract a large number of customers. These massive investments by a company are worthwhile because marketing tactics greatly influence the consumer behavior. Alternatively, these advertising campaigns have a discernible impact on recommendation systems which tend to promote popular items by ranking them at the top, resulting in biased and unfair decision making and loss of customers’ trust. The biasing impact of popularity of items on recommendations, however, is not fixed, and varies with time. Therefore, it is important to build a bias-aware recommendation system that can rank or predict items based on their true merit at given time frame. This thesis proposes a framework that can model the temporal bias of individual items defined by their characteristic contents, and provides a simple process for bias correction. Bias correction is done either by cleaning the bias from historical training data that is used for building predictive model, or by ignoring the estimated bias from the predictions of a standard predictor. Evaluated on two real world datasets, NetFlix and MovieLens, our framework is shown to be able to estimate and remove the bias as a result of adopted marketing techniques from the predicted popularity of items at a given time.

Twitter is one of today’s largest and most popular social networks. The users of the service generate huge amounts of data each day and rely heavily on the service helping them find interesting tweets in short time. The concept of hashtags aids in this practice but relies on the users choosing to include the correct and commonly used hashtags for the topic of their tweet. Hashtag recommendation has been a target of research before with varying results. This thesis proposes a method taking the location of the users into account when making recommen- dations. The method generated improved results over just using similar tweets as a basis for recommendation. Various factors like the handling of different variations of vocabulary in the tweets, how many tweets the suggestions can be picked from and how the combination of similarity and geographic ranking should function could affect the result. This leads to the conclusion that geographic data can be used to improve hashtag suggestions, but a different approach in handling similarity and alternative combinations of similarity and geographic ranking could cause another result. / Twitter är ett av nutidens största och populäraste sociala nätverk. Tjänstens användare producerar stora mängder data varje dag och förväntar sig att tjänsten ska kunna hjälpa dem att hitta intressanta tweets snabbt. Därmed finns konceptet med hashtags, men detta förutsätter att användare väljer att inkludera vanligt förekommande hashtags som på ett korrekt sätt avspeglar innehållet i tweeten. Automatisk rekommendation av hashtags har därmed varit ett populärt forskningsämne de senaste åren, med varierande resultat. Denna studie undersöker en rekommendationsmetod som väger in användarens geografiska position för att rekommendera så passande hashtags som möjligt. Resultaten visar att denna metod generellt rekommenderar mer passande hashtags än metoder som enbart rekommenderar hashtags genom att analysera likhet mellan tweets. Olika faktorer så som hanterandet av olika varianter av vokabulär, hur många tweets som metoden kan föreslå hashtags från samt hur kombinationen av rekommendation baserat på likhet och geografiskt position ska fungera, kan samtidigt påverka resultaten. Detta leder till slutsatsen att geografisk data kan användas för att förbättra hashtagrekommendation, men att ett annorlunda tillvägagångsätt i att hantera likhet och alternativa kombinationer av likhetsrangordning och geografisk rangordning kan leda till ett annorlunda resultat.

Nowadays people and organizations are more and more interconnected in the forms of social networks: the nodes are social entities and the links are various relationships among them. The social network theory and the methods of social network analysis (SNA) are being increasingly used to study such real-world networks in order to support knowledge management and decision making in organizations. However, most existing social network studies focus on the static topologies of networks. The dynamic network link formation process is largely ignored. This dissertation is devoted to study such dynamic network formation process to support knowledge management and decision making in networked environments. Three challenges remain to be addressed in modeling and analyzing the dynamic network link formation processes. The first challenge is about modeling the network topological changes using longitudinal network data. The second challenge is concerned with examining factors that influence formation of links among individuals in networks. The third challenge is regarding link prediction in evolving social networks. This dissertation presents four essays that address these challenges in various knowledge management domains. The first essay studies the topological changes of a major international terrorist network over a 14-year period. In addition, this paper used a simulation approach to examine this network's vulnerability to random failures, targeted attacks, and real world authorities' counterattacks. The second essay and third essay focuses on examining determinants that significantly influence the link formation processes in social networks. The second essay found that mutual acquaintance and vehicle affiliations facilitate future co-offending link formation in a real-world criminal network. The third essay found that homophily in programming language preference, and mutual are determinants for forming participation links in an online Open Source social network. The fourth essay focuses on the link prediction in evolving social networks. It proposes a novel infrastructure for describing and utilizing the discovered determinants of link formation process (i.e. semantics of social networks) in link prediction to support expert recommendation application in an Open Source developer community. It is found that the integrated mechanism outperforms either user-based or Top-N most recognized mechanism.

Coleções digitais de mídias, tanto pessoais como online, crescem rapidamente. Para que grandes quantidades de músicas sejam acessíveis à usuários, serviços populares como iTunes, Last.fm e Pandora oferecem recomendações. Essa abordagem livra usuários de lembrarem de músicas, e permite a descoberta de canções novas ou esquecidas. Mas recomendações apresentam problemas com usuários, como credibilidade e falta de controle. A motivação deste trabalho é melhorar a experiência de usuários com recomendações de música através do uso de explicações. Ao usar um sistema de recomendação, a satisfação e aprovação de usuários não depende só da eficácia do algoritmo, mas também de explicações. Pesquisas mostram que estas podem beneficiar sistemas de recomendação, aumentando a credibilidade e satisfação de usuários, ao oferecer mais transparência e formas de correção. O objetivo deste trabalho é projetar e desenvolver uma nova forma de visualização de tags, e testar sua viabilidade para explicar e filtrar recomendações de músicas. Mais precisamente, investigamos se esta visualização pode favorecer as metas de inspeção (scrutability), eficiência, eficácia e satisfação. A partir da pesquisa em necessidades de usuários para recomendações e música, a visualização Tag Strings foi projetada e desenvolvida. Tag Strings inclui tanto a interface da visualização, quanto o processo de coleta e cálculo de relevância de tags e músicas. Para a avaliação da visualização Tag Strings, dois tipos de experimentos foram construídos: a comparação entre uma lista de recomendações com Tag Strings, e a comparação entre o design de referência (baseado nos serviços Last.fm e Pandora) e Tag Strings. A construção desses dois experimentos permitiu a avaliação de Tag Strings como uma forma de explicação para recomendações de música. Os resultados dos experimentos evidenciam que a nova forma de visualização Tag Strings favorece as metas de inspeção (scrutability), eficiência, eficácia e satisfação, melhorando a usabilidade e experiência de usuários com recomendações de música. / Digital media collections, both personal and online, grow rapidly. To make large music collections available to users, popular services such as iTunes, Last.fm and Pandora offer recommendations. This approach frees users from searching for music, and allows for the discovery of new or forgotten items. But recommendations present issues such as user trust and lack of control. The motivation for this project is to improve user experience with music recommendations through explanations. While using a recommendation system, user acceptance and satisfaction depends not only on the algorithm effectiveness, but also on explanations. Research shows that recommendations benefit from explanations, increasing user trust and satisfaction by offering more transparency and scrutability. The goal of this project is to design and develop a new form of tag visualization, and test its feasibility to explain and filter music recommendations. We specifically investigate if the visualization can support the aims of scrutability, efficiency, effectiveness and satisfaction. Based on the user research and needs for music recommendation, the visualization Tag Strings was designed and developed. Tag Strings includes both the visualization interface and the process of collecting and calculation of tag and track relevancy. To evaluate the visualization Tag String, we designed two types of experiments: comparing Tag Strings with a recommendation list, and comparing Tag Strings with a design reference (based on the services Last.fm and Pandora). The design of these two experiments allowed the evaluation of Tag Strings as a form of explanation to music recommendation. The experiment results highlight that the new visualization Tag Strings favors the aims of scrutability, efficiency, effectiveness and satisfaction, improving the user experience with music recommendations.